Size and distortion invariant object recognition by hierarchical graph matching

A neural system is presented for invariant object recognition. Its flexibility is demonstrated with freely taken camera images of human faces. The system is an application of the dynamic link architecture, which owes its strength to an enhancement of tradition neural networks by a new kind of variable to express the hierarchical grouping of neurons. This capability is used to group primitive local feature detectors (Gabor-based wavelets) into composite feature detectors (jets) and to preserve neighborhood relationships between jets when they lose position information on the way from the image domain to the object domain. Due to the potential for grouping, objects can be represented as attributed graphs, with jets serving as attributes. Recognition is formulated as graph matching and is implemented as a topologically constrained diffusion of image-object links. A hierarchical sequence of matches, from low-frequency components of jets to high-frequency components, is used. Size invariance is achieved by interposing diffusion steps in magnification space. The system is implemented on a network of transputers